meetscribe-offline 0.4.1

Fully local meeting transcription with speaker diarization, AI summaries, and PDF output

meetscribe

Fully local meeting transcription with speaker diarization, AI-generated summaries, and professional PDF output.

Records dual-channel audio (your mic + system audio) from any meeting app and produces diarized transcripts using WhisperX + pyannote-audio. Works fully offline with local models, or optionally use cloud APIs (OpenRouter, Claude Max) for higher-quality summaries.

Works with any meeting app

Because meetscribe captures system audio at the OS level, it works with every voice/video call application:

• Zoom
• Google Meet
• Microsoft Teams
• Slack (huddles and calls)
• Discord
• Signal (voice and video calls)
• Telegram (voice and video calls)
• WhatsApp (desktop voice and video calls)
• Keet (P2P calls)
• Jitsi Meet
• Webex
• Skype
• FaceTime (via browser)
• GoTo Meeting
• RingCentral
• Amazon Chime
• BlueJeans

Any app that plays audio through your system speakers will work -- including browser-based meetings and standalone desktop clients.

Features

• Dual-channel audio capture -- records your mic (left channel) and remote participants (right channel) simultaneously via PipeWire/PulseAudio + ffmpeg
• WhisperX transcription -- fast batched inference with openai/whisper-large-v3-turbo, word-level timestamps via wav2vec2 alignment
• Multilingual -- auto-detects language or manually set it; supports English, German, Turkish, French, Spanish, Farsi, and 90+ other languages
• Speaker diarization -- pyannote-audio identifies who said what, with automatic YOU/REMOTE labeling from the dual-channel signal
• AI meeting summaries -- local LLMs via Ollama, or cloud APIs via OpenRouter / Claude Max, with automatic fallback between backends
• Voiceprint speaker recognition -- automatically identifies speakers across meetings using voice embedding profiles
• Meeting sync -- push transcripts and summaries to any Git repository on a configurable schedule
• Professional PDF output -- summary + full transcript in a clean, page-numbered PDF with full Unicode support (DejaVu Sans) and RTL for Farsi
• Multiple output formats -- .txt, .srt, .json, .summary.md, .pdf
• GTK3 GUI widget -- small always-on-top window with record/stop, timer, and one-click access to results
• CLI -- meet record, meet transcribe, meet run, meet gui, meet label, meet enroll, meet sync, meet devices, meet check
• Per-session folders -- each recording gets its own organized directory
• Offline-first -- after initial model download, core features work without internet; cloud backends are optional upgrades

Quick start

# Install from PyPI
pip install meetscribe-offline

# Set your HuggingFace token (required for speaker diarization)
export HF_TOKEN=hf_your_token_here

# Record a meeting, then auto-transcribe + summarize when you stop
meet run
# Press Ctrl+C when the meeting ends

Requirements

• Linux with PipeWire or PulseAudio
• NVIDIA GPU with CUDA (8GB+ VRAM recommended; CPU mode available but slower)
• Python 3.10+
• ffmpeg
• HuggingFace token (free) for the diarization model
• Ollama (optional) for AI meeting summaries

See REQUIREMENTS.md for full hardware/software details.

Installation

1. System dependencies

# Ubuntu / Pop!_OS / Debian
sudo apt install ffmpeg pulseaudio-utils

# Fedora
sudo dnf install ffmpeg pulseaudio-utils

2. Install meetscribe

# From PyPI (recommended)
pip install meetscribe-offline

# From source
git clone https://github.com/pretyflaco/meetscribe
cd meetscribe
pip install -e .

This creates the meet command in your PATH.

3. HuggingFace token (for speaker diarization)

1. Create a free account at https://huggingface.co
2. Accept the model terms at https://huggingface.co/pyannote/speaker-diarization-community-1
3. Create a read token at https://huggingface.co/settings/tokens
4. Set it:

export HF_TOKEN=hf_your_token_here
# Add to ~/.bashrc for persistence:
echo 'export HF_TOKEN=hf_your_token_here' >> ~/.bashrc

4. Ollama (optional, for AI summaries)

Install from https://ollama.com, then pull the default summary model:

ollama pull qwen3.5:9b

5. Verify setup

meet check

Usage

Check audio devices

meet devices

Record a meeting

Start recording before or during your meeting:

meet record

Press Ctrl+C when the meeting ends. A 10-second drain buffer ensures all audio is captured. Recordings are saved to ~/meet-recordings/.

Options:

• -o /path -- save recordings elsewhere
• --virtual-sink -- create isolated virtual sink (avoids capturing notification sounds)
• --mic <source> -- specify mic source (use meet devices to find names)
• --monitor <source> -- specify monitor source

Transcribe a recording

meet transcribe ~/meet-recordings/meeting-20260312-140000/meeting-20260312-140000.wav

Options:

• -m large-v3-turbo -- Whisper model (default: large-v3-turbo; also: base, medium, large-v2)
• -l auto -- language code or auto to auto-detect (default: auto; e.g. en, de, tr, fa)
• --device cuda -- cuda or cpu (default: cuda)
• --compute-type float16 -- float16 or int8 for lower VRAM (default: float16)
• -b 16 -- batch size, reduce if running low on VRAM (default: 16)
• --min-speakers 2 / --max-speakers 6 -- hint for number of speakers
• --no-diarize -- skip speaker diarization
• --no-summarize -- skip AI summary generation
• --summary-backend openrouter -- summary backend (ollama, openrouter, claudemax, openai)
• --summary-model <model> -- model for summary (default: per-backend)
• --skip-alignment -- skip word-level alignment (useful if alignment model is unavailable)
• --mixdown mono|dual -- stereo mixdown mode (default: mono). Use dual for headphone setups where mic and system audio don't bleed into each other (see below)

Dual-channel mode for headphone users

If you use headphones, your mic captures only your voice while the system channel captures only the remote participants. In this setup the default mono mixdown creates a ~20× energy imbalance that causes WhisperX to suppress the quieter voice.

Use --mixdown dual to transcribe each channel independently:

meet transcribe --mixdown dual ~/meet-recordings/meeting-20260312-140000/

This skips diarization entirely (channel identity = speaker identity) and labels segments as YOU (mic) or REMOTE (system). Default --mixdown mono behavior is unchanged -- use it when your speakers play into the room and both voices appear on both channels.

Record + transcribe in one shot

meet run

Records until Ctrl+C, then automatically transcribes, generates a summary, and produces a PDF. Takes all options from both record and transcribe (including --mixdown dual).

Launch the GUI widget

meet gui

A small always-on-top window with:

• Record / Stop button
• Live timer and file size
• Status indicator (Recording, Flushing, Transcribing, Summarizing, Done)
• "Open PDF" and "Open Folder" buttons after completion

When 2 or more speakers are detected, a speaker labeling dialog appears before the results are saved. Each speaker is shown with their channel and a sample line of text. If voice profiles exist, confident matches are shown automatically. Enter a real name or leave blank to keep the auto-assigned label (YOU, REMOTE_1, etc.).

If meeting sync is configured and the recording matches a scheduled meeting, a sync confirmation prompt appears with Push / Skip buttons.

Label speakers after the fact

meet label ~/meet-recordings/meeting-20260313-214133

For each speaker in the recording, meet label:

1. Shows a table of all speakers (label, channel, segment count, sample text)
2. Plays a short audio clip from that speaker's channel (requires ffplay)
3. Prompts you to enter a real name (press Enter to keep the existing label)
4. Regenerates all outputs (.txt, .srt, .json, .summary.md, .pdf) with the new names

With --auto, voice profiles are used to automatically identify known speakers. Confident matches are applied without prompting; only unrecognized speakers get the interactive prompt:

meet label --auto ~/meet-recordings/meeting-20260313-214133

Options:

• --auto -- auto-label using voice profiles (see Voiceprint speaker recognition)
• --no-audio -- skip audio playback, just show text samples
• --no-summary -- use find-and-replace instead of re-running Ollama
• --summary-backend / --summary-model -- override summary backend and model for regeneration

Output

Each recording gets its own session directory:

~/meet-recordings/meeting-20260312-140000/
    meeting-20260312-140000.wav            # Stereo audio (16kHz)
    meeting-20260312-140000.session.json   # Recording metadata
    meeting-20260312-140000.ffmpeg.log     # ffmpeg capture log
    meeting-20260312-140000.txt            # Plain text transcript
    meeting-20260312-140000.srt            # Subtitle format
    meeting-20260312-140000.json           # Full detail (word-level timestamps)
    meeting-20260312-140000.summary.md     # AI meeting summary (Markdown)
    meeting-20260312-140000.pdf            # Professional PDF (summary + transcript)

Example .txt output:

[00:00:12 --> 00:00:18] YOU: So the main issue we're seeing is with the API rate limiting.
[00:00:19 --> 00:00:25] REMOTE_1: Right, I think we should implement exponential backoff.
[00:00:26 --> 00:00:31] YOU: Agreed. Can you also look at caching the responses?

AI summary

meetscribe generates a structured meeting summary with:

• Overview
• Key topics discussed
• Action items (with owners when mentioned)
• Decisions made
• Open questions / follow-ups

Supported models

Model	Size	Speed	Notes
qwen3.5:9b	6.6 GB	~18-35s	Default -- best balance of quality and speed
gemma3:12b	8.1 GB	~15s	Fastest
qwen3:14b	9.3 GB	~39s	Good quality
glm-4.7-flash	19 GB	~37s	Must use thinking-off mode (handled automatically)

Change the model:

meet run --summary-model gemma3:12b

Disable summaries:

meet run --no-summarize

Summary backends

meetscribe supports three backends with automatic fallback:

Backend	Setup	Cost	Quality
ollama (default)	ollama serve + ollama pull qwen3.5:9b	Free	Good
openrouter	Set OPENROUTER_API_KEY	Pay-per-use	Excellent
claudemax	Run claude-max-api-proxy on localhost:3457	Claude Max subscription	Excellent
openai	Set MEETSCRIBE_OPENAI_BASE_URL	Varies	Varies

The openai backend works with any OpenAI-compatible API — Lemonade, LiteLLM, vLLM, text-generation-webui, LocalAI, or any self-hosted endpoint.

# Use OpenRouter
meet run --summary-backend openrouter --summary-model anthropic/claude-sonnet-4.6

# Use any OpenAI-compatible endpoint
export MEETSCRIBE_SUMMARY_BACKEND=openai
export MEETSCRIBE_OPENAI_BASE_URL=http://localhost:8000/v1
export MEETSCRIBE_SUMMARY_MODEL=your-model-name
# Optional: export MEETSCRIBE_OPENAI_API_KEY=your-key

# Or set via environment variables
export MEETSCRIBE_SUMMARY_BACKEND=openrouter
export MEETSCRIBE_SUMMARY_MODEL=anthropic/claude-sonnet-4.6

If the configured backend is unavailable, meetscribe automatically tries the next one in the fallback chain: claudemax → openrouter → ollama.

Customizing the prompt

The summarization prompt lives in meet/prompts/summarize_system.md. Edit it to change the summary format, add domain-specific instructions, or tune for your preferred model. No Python changes needed.

Voiceprint speaker recognition

meetscribe can automatically identify speakers across meetings using voice embeddings. After you label speakers in one meeting, their voice profiles are stored and matched against future recordings.

# Build profiles from already-labeled sessions
meet enroll ~/meet-recordings/meeting-20260330-*

# Auto-label speakers in future meetings using voice profiles
meet label --auto ~/meet-recordings/meeting-20260401-093000

Profiles are stored in ~/.config/meet/speaker_profiles.json and improve with each labeled session (running average of embeddings).

Meeting sync

Push meeting artifacts to a Git repository on a configurable schedule.

# Create an example config
meet sync --init-config
# Edit ~/.config/meet/sync_config.json with your repo URL and schedule

# Push a session manually
meet sync ~/meet-recordings/meeting-20260331-110038_STANDUP

# View configured schedule
meet sync --list-schedule

When the GUI detects a matching scheduled meeting, it prompts for confirmation before syncing. Sessions that don't match the schedule are skipped. The CLI uses --force to sync unmatched sessions.

You can also configure a team_members list and min_team_members threshold in sync_config.json to require that a minimum number of recognized speakers are present before offering to sync.

Multilingual support

meetscribe auto-detects the spoken language by default (Whisper large-v3-turbo supports 99 languages). You can also set it explicitly:

meet run --language de       # German
meet run --language tr       # Turkish
meet run --language fr       # French
meet run --language es       # Spanish
meet run --language fa       # Farsi (Persian)
meet run --language auto     # Auto-detect (default)

How it works

• Transcription: The same Whisper model handles all languages -- no extra download or VRAM cost. When set to auto, the detected language is used for alignment and all downstream steps.
• Speaker diarization: Completely language-agnostic (based on voice characteristics, not speech content).
• AI summary: When a non-English language is detected, the summary prompt instructs the LLM to write the summary in the same language as the transcript.
• PDF output: Uses DejaVu Sans for full Unicode coverage (Latin, Cyrillic, Greek, Turkish special characters, etc.). Farsi uses Noto Naskh Arabic with RTL text reshaping.

Tested languages

Language	Code	Alignment model	PDF font	Notes
English	en	wav2vec2 (torchaudio)	DejaVu Sans
German	de	VoxPopuli (torchaudio)	DejaVu Sans
French	fr	VoxPopuli (torchaudio)	DejaVu Sans
Spanish	es	VoxPopuli (torchaudio)	DejaVu Sans
Turkish	tr	wav2vec2 (HuggingFace)	DejaVu Sans	~1.2 GB alignment model download
Farsi	fa	wav2vec2 (HuggingFace)	Noto Naskh Arabic	~1.2 GB alignment model download, RTL

Farsi RTL requirements

Farsi uses right-to-left text. For proper PDF rendering, install the optional RTL dependencies:

pip install arabic-reshaper python-bidi
# Or with the optional extra:
pip install "meetscribe-offline[rtl]"

Without these libraries, Farsi text will appear in the PDF but glyphs may not be joined correctly and reading order may be wrong.

Virtual sink mode

By default, meet record captures all system audio (including notification sounds, music, etc.). For cleaner recordings, use --virtual-sink:

meet record --virtual-sink

This creates an isolated audio sink. Route your meeting app's audio to it:

1. Open pavucontrol (PulseAudio Volume Control)
2. Go to the "Playback" tab
3. Find your browser or meeting app
4. Change its output to "Meet-Capture"

You'll still hear the meeting through your normal speakers via automatic loopback.

VRAM usage

With an NVIDIA GPU (12 GB VRAM):

Model	Transcription	+ Diarization	Recommended batch_size
large-v3-turbo	~4 GB	~7 GB total	16
medium	~3 GB	~6 GB total	16
base	~1 GB	~4 GB total	16

If you hit OOM errors:

1. Reduce --batch-size to 4 or 8
2. Use --compute-type int8
3. Use a smaller model (--model medium or --model base)
4. Use --device cpu as a last resort

How it works

[Meeting App] --> [PipeWire/PulseAudio] --> [ffmpeg dual-channel capture] --> meeting.wav
                                                                                  |
                  [WhisperX: faster-whisper + wav2vec2 alignment + pyannote diarization]
                                                                                  |
                                      [Ollama LLM summary]     [Diarized transcript]
                                              |                         |
                                        .summary.md          .txt / .srt / .json
                                              |                         |
                                              +--------> .pdf <---------+

Capture: Records your mic (left channel) and system audio (right channel) simultaneously into a single stereo WAV file at 16 kHz.

Transcribe: Runs the WhisperX pipeline -- batched Whisper transcription, wav2vec2 forced alignment for word-level timestamps, and pyannote speaker diarization. Dual-channel energy analysis maps speakers to YOU or REMOTE.

Summarize: Sends the transcript to a local Ollama model that extracts a structured summary.

PDF: Combines the summary and full transcript into a professional page-numbered PDF document.

CUDA NVRTC note

The pyannote diarization model requires CUDA NVRTC for JIT compilation. If your CUDA driver version doesn't match the installed libnvrtc-builtins version, meetscribe automatically creates a compatibility symlink. This happens transparently on first use.

If you still see NVRTC errors:

export LD_LIBRARY_PATH=$HOME/.local/lib/cuda:$LD_LIBRARY_PATH

Limitations

• Overlapping speech is not handled well (Whisper limitation)
• Speaker labels default to role-based (YOU, REMOTE_1, REMOTE_2) — use meet label or the GUI dialog to assign real names
• Diarization accuracy varies with audio quality and number of speakers
• Linux only (PulseAudio/PipeWire dependency)

Contributing

git clone https://github.com/pretyflaco/meetscribe
cd meetscribe
pip install -e .[dev]
/usr/bin/python3 -m pytest tests/

Pull requests welcome. Please run the test suite before submitting.

Changelog

See CHANGELOG.md for release history.

License

GPL-3.0